Master Thesis Performance Evaluation of CPUs, GPUs, and NPUs on Time-Series ML Applications (exjobb)

Location:
Ludvika, Dalarna County, Sweden

Job ID:
R0071409

Date Posted:
2025-01-08

Company Name:
HITACHI ENERGY SWEDEN AB

Profession (Job Category):
Administration & Facilities

Job Schedule:
Part time

Remote:
No

Job Description:
The Opportunity

We are looking for a master thesis student at our R&D department, would like to join our multinational organization with lots of development opportunities. Our team is dedicated to creating a future where innovative digital technology brings cleaner energy to people around the world. If this opportunity excites you, we encourage you to apply!

How you'll make an impact

• Determine a suitable array of time series machine learning models, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and possibly transformer-based models. Implement these models modularly to enable their deployment on a variety of hardware types.
• Configure and optimize the three processor environments CPU, GPU, and NPU by employing industry standard frameworks like TensorFlow and PyTorch, ensuring that each setup is tailored for effective benchmarking.
• Defining performance metrics and benchmarks requires to pinpoint key indicators such as training time, inference speed, energy consumption, and memory usage. We will benchmark each hardware type across chosen applications, with a keen focus on comparing processing times for small, medium, and large datasets, and evaluating the scalability of each processor type.
• Recommendations and guidelines based on the findings, offer practical guidelines on hardware selection for different types of time series applications. Discuss trade-offs between performance, cost, and scalability.

Your background

• Bachelor’s degree in computer science, electrical engineering, data science, or a related field.
• Proficiency in python, with experience in deep learning libraries (e.g. TensorFlow, PyTorch) and familiarity with hardware configuration for machine learning tasks.
• Knowledge of time series analysis and machine learning algorithms, particularly recurrent and convolutional neural networks.
• Basic understanding of CPUs, GPUs, and NPUs, as well as parallel computing principles.
• Prior experience in machine learning model deployment, optimization, or hardware benchmarking would be beneficial.

More about us

Are you ready for an exciting new challenge? Does the above description sound like you? Applications will be reviewed on an ongoing basis, so don’t delay – apply today! Recruiting Manager: Lucas Bottura, [email protected] and thesis supervisor: Hasan Basri Celebi, [email protected] will answer your questions about the position.

Don’t hesitate – apply today and let us learn more about you and the unique contributions you can bring to our team.

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Location: (HE)Office_Ludvika Lyviksvagen 3

Additional location:

• Vaesteras, Vastmanland County, Sweden